Overview

Dataset statistics

Number of variables29
Number of observations19955
Missing cells48218
Missing cells (%)8.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.4 MiB
Average record size in memory232.0 B

Variable types

Categorical16
DateTime2
Numeric10
Text1

Alerts

SATISFACTION has 4084 (20.5%) missing valuesMissing
COMMUNICATION has 4020 (20.1%) missing valuesMissing
GOALS has 3833 (19.2%) missing valuesMissing
DELIVERABLES has 4083 (20.5%) missing valuesMissing
TIMELINESS has 3986 (20.0%) missing valuesMissing
CHALLENGES has 4154 (20.8%) missing valuesMissing
PROJECTMANAGEMENT has 4155 (20.8%) missing valuesMissing
SUPPORT has 3917 (19.6%) missing valuesMissing
IMPROVEMENT has 15986 (80.1%) missing valuesMissing

Reproduction

Analysis started2024-04-16 12:11:04.833635
Analysis finished2024-04-16 12:11:21.002616
Duration16.17 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

PROJECTASSIGNED
Categorical

Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.0 KiB
Cox-Hays Solution
 
1113
White LLC Initiative
 
1109
Miller LLC Initiative
 
1091
Robinson and Sons Project
 
1072
Hicks and Sons Initiative
 
1063
Other values (15)
14507 

Length

Max length38
Median length32
Mean length24.661188
Min length17

Characters and Unicode

Total characters492114
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWashington-Little Initiative
2nd rowNeal PLC Initiative
3rd rowNeal PLC Initiative
4th rowWashington-Little Initiative
5th rowWashington-Little Initiative

Common Values

ValueCountFrequency (%)
Cox-Hays Solution 1113
 
5.6%
White LLC Initiative 1109
 
5.6%
Miller LLC Initiative 1091
 
5.5%
Robinson and Sons Project 1072
 
5.4%
Hicks and Sons Initiative 1063
 
5.3%
Neal PLC Initiative 1045
 
5.2%
Willis Ltd Solution 1040
 
5.2%
Jenkins-Johnson Initiative 1016
 
5.1%
Villarreal-Flynn Project 1008
 
5.1%
Johnson, Roth and Ray Initiative 1007
 
5.0%
Other values (10) 9391
47.1%

Length

2024-04-16T17:41:21.160616image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
initiative 9991
 
16.1%
and 5994
 
9.7%
project 5923
 
9.6%
solution 4041
 
6.5%
llc 2200
 
3.5%
sons 2135
 
3.4%
miller 2091
 
3.4%
cox-hays 1113
 
1.8%
white 1109
 
1.8%
robinson 1072
 
1.7%
Other values (27) 26330
42.5%

Most occurring characters

ValueCountFrequency (%)
i 52020
 
10.6%
n 46282
 
9.4%
42044
 
8.5%
t 38704
 
7.9%
o 33690
 
6.8%
a 33319
 
6.8%
e 30585
 
6.2%
l 21589
 
4.4%
r 20471
 
4.2%
s 16905
 
3.4%
Other values (35) 156505
31.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 492114
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 52020
 
10.6%
n 46282
 
9.4%
42044
 
8.5%
t 38704
 
7.9%
o 33690
 
6.8%
a 33319
 
6.8%
e 30585
 
6.2%
l 21589
 
4.4%
r 20471
 
4.2%
s 16905
 
3.4%
Other values (35) 156505
31.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 492114
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 52020
 
10.6%
n 46282
 
9.4%
42044
 
8.5%
t 38704
 
7.9%
o 33690
 
6.8%
a 33319
 
6.8%
e 30585
 
6.2%
l 21589
 
4.4%
r 20471
 
4.2%
s 16905
 
3.4%
Other values (35) 156505
31.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 492114
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 52020
 
10.6%
n 46282
 
9.4%
42044
 
8.5%
t 38704
 
7.9%
o 33690
 
6.8%
a 33319
 
6.8%
e 30585
 
6.2%
l 21589
 
4.4%
r 20471
 
4.2%
s 16905
 
3.4%
Other values (35) 156505
31.8%

SATISFACTION
Categorical

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing4084
Missing (%)20.5%
Memory size156.0 KiB
2.0
3329 
4.0
3280 
5.0
3157 
3.0
3082 
1.0
3023 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters47613
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
2.0 3329
16.7%
4.0 3280
16.4%
5.0 3157
15.8%
3.0 3082
15.4%
1.0 3023
15.1%
(Missing) 4084
20.5%

Length

2024-04-16T17:41:21.341617image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:41:21.530617image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 3329
21.0%
4.0 3280
20.7%
5.0 3157
19.9%
3.0 3082
19.4%
1.0 3023
19.0%

Most occurring characters

ValueCountFrequency (%)
. 15871
33.3%
0 15871
33.3%
2 3329
 
7.0%
4 3280
 
6.9%
5 3157
 
6.6%
3 3082
 
6.5%
1 3023
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47613
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 15871
33.3%
0 15871
33.3%
2 3329
 
7.0%
4 3280
 
6.9%
5 3157
 
6.6%
3 3082
 
6.5%
1 3023
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47613
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 15871
33.3%
0 15871
33.3%
2 3329
 
7.0%
4 3280
 
6.9%
5 3157
 
6.6%
3 3082
 
6.5%
1 3023
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47613
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 15871
33.3%
0 15871
33.3%
2 3329
 
7.0%
4 3280
 
6.9%
5 3157
 
6.6%
3 3082
 
6.5%
1 3023
 
6.3%

COMMUNICATION
Categorical

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing4020
Missing (%)20.1%
Memory size156.0 KiB
5.0
3349 
4.0
3192 
2.0
3192 
1.0
3164 
3.0
3038 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters47805
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row4.0
4th row5.0
5th row3.0

Common Values

ValueCountFrequency (%)
5.0 3349
16.8%
4.0 3192
16.0%
2.0 3192
16.0%
1.0 3164
15.9%
3.0 3038
15.2%
(Missing) 4020
20.1%

Length

2024-04-16T17:41:21.735613image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:41:21.884613image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
5.0 3349
21.0%
4.0 3192
20.0%
2.0 3192
20.0%
1.0 3164
19.9%
3.0 3038
19.1%

Most occurring characters

ValueCountFrequency (%)
. 15935
33.3%
0 15935
33.3%
5 3349
 
7.0%
4 3192
 
6.7%
2 3192
 
6.7%
1 3164
 
6.6%
3 3038
 
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47805
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 15935
33.3%
0 15935
33.3%
5 3349
 
7.0%
4 3192
 
6.7%
2 3192
 
6.7%
1 3164
 
6.6%
3 3038
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47805
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 15935
33.3%
0 15935
33.3%
5 3349
 
7.0%
4 3192
 
6.7%
2 3192
 
6.7%
1 3164
 
6.6%
3 3038
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47805
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 15935
33.3%
0 15935
33.3%
5 3349
 
7.0%
4 3192
 
6.7%
2 3192
 
6.7%
1 3164
 
6.6%
3 3038
 
6.4%

GOALS
Categorical

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing3833
Missing (%)19.2%
Memory size156.0 KiB
1.0
3266 
3.0
3265 
4.0
3232 
5.0
3226 
2.0
3133 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters48366
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row2.0
3rd row2.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
1.0 3266
16.4%
3.0 3265
16.4%
4.0 3232
16.2%
5.0 3226
16.2%
2.0 3133
15.7%
(Missing) 3833
19.2%

Length

2024-04-16T17:41:22.054631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:41:22.207615image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3266
20.3%
3.0 3265
20.3%
4.0 3232
20.0%
5.0 3226
20.0%
2.0 3133
19.4%

Most occurring characters

ValueCountFrequency (%)
. 16122
33.3%
0 16122
33.3%
1 3266
 
6.8%
3 3265
 
6.8%
4 3232
 
6.7%
5 3226
 
6.7%
2 3133
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48366
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 16122
33.3%
0 16122
33.3%
1 3266
 
6.8%
3 3265
 
6.8%
4 3232
 
6.7%
5 3226
 
6.7%
2 3133
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48366
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 16122
33.3%
0 16122
33.3%
1 3266
 
6.8%
3 3265
 
6.8%
4 3232
 
6.7%
5 3226
 
6.7%
2 3133
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48366
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 16122
33.3%
0 16122
33.3%
1 3266
 
6.8%
3 3265
 
6.8%
4 3232
 
6.7%
5 3226
 
6.7%
2 3133
 
6.5%

DELIVERABLES
Categorical

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing4083
Missing (%)20.5%
Memory size156.0 KiB
5.0
3366 
1.0
3274 
4.0
3104 
2.0
3069 
3.0
3059 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters47616
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row5.0
3rd row3.0
4th row1.0
5th row4.0

Common Values

ValueCountFrequency (%)
5.0 3366
16.9%
1.0 3274
16.4%
4.0 3104
15.6%
2.0 3069
15.4%
3.0 3059
15.3%
(Missing) 4083
20.5%

Length

2024-04-16T17:41:22.382616image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:41:22.533630image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
5.0 3366
21.2%
1.0 3274
20.6%
4.0 3104
19.6%
2.0 3069
19.3%
3.0 3059
19.3%

Most occurring characters

ValueCountFrequency (%)
. 15872
33.3%
0 15872
33.3%
5 3366
 
7.1%
1 3274
 
6.9%
4 3104
 
6.5%
2 3069
 
6.4%
3 3059
 
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47616
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 15872
33.3%
0 15872
33.3%
5 3366
 
7.1%
1 3274
 
6.9%
4 3104
 
6.5%
2 3069
 
6.4%
3 3059
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47616
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 15872
33.3%
0 15872
33.3%
5 3366
 
7.1%
1 3274
 
6.9%
4 3104
 
6.5%
2 3069
 
6.4%
3 3059
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47616
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 15872
33.3%
0 15872
33.3%
5 3366
 
7.1%
1 3274
 
6.9%
4 3104
 
6.5%
2 3069
 
6.4%
3 3059
 
6.4%

TIMELINESS
Categorical

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing3986
Missing (%)20.0%
Memory size156.0 KiB
2.0
3261 
1.0
3247 
3.0
3194 
5.0
3146 
4.0
3121 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters47907
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row3.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
2.0 3261
16.3%
1.0 3247
16.3%
3.0 3194
16.0%
5.0 3146
15.8%
4.0 3121
15.6%
(Missing) 3986
20.0%

Length

2024-04-16T17:41:22.709631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:41:22.859633image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 3261
20.4%
1.0 3247
20.3%
3.0 3194
20.0%
5.0 3146
19.7%
4.0 3121
19.5%

Most occurring characters

ValueCountFrequency (%)
. 15969
33.3%
0 15969
33.3%
2 3261
 
6.8%
1 3247
 
6.8%
3 3194
 
6.7%
5 3146
 
6.6%
4 3121
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47907
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 15969
33.3%
0 15969
33.3%
2 3261
 
6.8%
1 3247
 
6.8%
3 3194
 
6.7%
5 3146
 
6.6%
4 3121
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47907
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 15969
33.3%
0 15969
33.3%
2 3261
 
6.8%
1 3247
 
6.8%
3 3194
 
6.7%
5 3146
 
6.6%
4 3121
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47907
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 15969
33.3%
0 15969
33.3%
2 3261
 
6.8%
1 3247
 
6.8%
3 3194
 
6.7%
5 3146
 
6.6%
4 3121
 
6.5%

CHALLENGES
Categorical

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing4154
Missing (%)20.8%
Memory size156.0 KiB
4.0
3232 
1.0
3194 
2.0
3190 
5.0
3102 
3.0
3083 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters47403
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row1.0
4th row4.0
5th row2.0

Common Values

ValueCountFrequency (%)
4.0 3232
16.2%
1.0 3194
16.0%
2.0 3190
16.0%
5.0 3102
15.5%
3.0 3083
15.4%
(Missing) 4154
20.8%

Length

2024-04-16T17:41:23.038639image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:41:23.194632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
4.0 3232
20.5%
1.0 3194
20.2%
2.0 3190
20.2%
5.0 3102
19.6%
3.0 3083
19.5%

Most occurring characters

ValueCountFrequency (%)
. 15801
33.3%
0 15801
33.3%
4 3232
 
6.8%
1 3194
 
6.7%
2 3190
 
6.7%
5 3102
 
6.5%
3 3083
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47403
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 15801
33.3%
0 15801
33.3%
4 3232
 
6.8%
1 3194
 
6.7%
2 3190
 
6.7%
5 3102
 
6.5%
3 3083
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47403
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 15801
33.3%
0 15801
33.3%
4 3232
 
6.8%
1 3194
 
6.7%
2 3190
 
6.7%
5 3102
 
6.5%
3 3083
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47403
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 15801
33.3%
0 15801
33.3%
4 3232
 
6.8%
1 3194
 
6.7%
2 3190
 
6.7%
5 3102
 
6.5%
3 3083
 
6.5%

PROJECTMANAGEMENT
Categorical

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing4155
Missing (%)20.8%
Memory size156.0 KiB
5.0
3418 
3.0
3187 
4.0
3151 
2.0
3139 
1.0
2905 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters47400
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row5.0
3rd row3.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
5.0 3418
17.1%
3.0 3187
16.0%
4.0 3151
15.8%
2.0 3139
15.7%
1.0 2905
14.6%
(Missing) 4155
20.8%

Length

2024-04-16T17:41:23.367631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:41:23.514635image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
5.0 3418
21.6%
3.0 3187
20.2%
4.0 3151
19.9%
2.0 3139
19.9%
1.0 2905
18.4%

Most occurring characters

ValueCountFrequency (%)
. 15800
33.3%
0 15800
33.3%
5 3418
 
7.2%
3 3187
 
6.7%
4 3151
 
6.6%
2 3139
 
6.6%
1 2905
 
6.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 15800
33.3%
0 15800
33.3%
5 3418
 
7.2%
3 3187
 
6.7%
4 3151
 
6.6%
2 3139
 
6.6%
1 2905
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 15800
33.3%
0 15800
33.3%
5 3418
 
7.2%
3 3187
 
6.7%
4 3151
 
6.6%
2 3139
 
6.6%
1 2905
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 15800
33.3%
0 15800
33.3%
5 3418
 
7.2%
3 3187
 
6.7%
4 3151
 
6.6%
2 3139
 
6.6%
1 2905
 
6.1%

SUPPORT
Categorical

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing3917
Missing (%)19.6%
Memory size156.0 KiB
4.0
3500 
5.0
3224 
2.0
3180 
1.0
3082 
3.0
3052 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters48114
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row5.0
3rd row2.0
4th row5.0
5th row1.0

Common Values

ValueCountFrequency (%)
4.0 3500
17.5%
5.0 3224
16.2%
2.0 3180
15.9%
1.0 3082
15.4%
3.0 3052
15.3%
(Missing) 3917
19.6%

Length

2024-04-16T17:41:23.687631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:41:23.859614image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
4.0 3500
21.8%
5.0 3224
20.1%
2.0 3180
19.8%
1.0 3082
19.2%
3.0 3052
19.0%

Most occurring characters

ValueCountFrequency (%)
. 16038
33.3%
0 16038
33.3%
4 3500
 
7.3%
5 3224
 
6.7%
2 3180
 
6.6%
1 3082
 
6.4%
3 3052
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48114
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 16038
33.3%
0 16038
33.3%
4 3500
 
7.3%
5 3224
 
6.7%
2 3180
 
6.6%
1 3082
 
6.4%
3 3052
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48114
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 16038
33.3%
0 16038
33.3%
4 3500
 
7.3%
5 3224
 
6.7%
2 3180
 
6.6%
1 3082
 
6.4%
3 3052
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48114
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 16038
33.3%
0 16038
33.3%
4 3500
 
7.3%
5 3224
 
6.7%
2 3180
 
6.6%
1 3082
 
6.4%
3 3052
 
6.3%

IMPROVEMENT
Categorical

MISSING 

Distinct10
Distinct (%)0.3%
Missing15986
Missing (%)80.1%
Memory size156.0 KiB
Encourage cross-functional collaboration.
458 
Conduct regular retrospectives for learning.
455 
Clarify project scopes to avoid creep.
415 
Explore new project management tools.
408 
Improve inter-team communication.
405 
Other values (5)
1828 

Length

Max length47
Median length44
Mean length41.034769
Min length33

Characters and Unicode

Total characters162867
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEnhance code reviews for early issue detection.
2nd rowOffer more training for team skill enhancement.
3rd rowEncourage cross-functional collaboration.
4th rowConduct regular retrospectives for learning.
5th rowClarify project scopes to avoid creep.

Common Values

ValueCountFrequency (%)
Encourage cross-functional collaboration. 458
 
2.3%
Conduct regular retrospectives for learning. 455
 
2.3%
Clarify project scopes to avoid creep. 415
 
2.1%
Explore new project management tools. 408
 
2.0%
Improve inter-team communication. 405
 
2.0%
Strengthen risk assessment strategies. 390
 
2.0%
Offer more training for team skill enhancement. 388
 
1.9%
Implement better documentation practices. 367
 
1.8%
Upgrade testing infrastructure and automation. 366
 
1.8%
Enhance code reviews for early issue detection. 317
 
1.6%
(Missing) 15986
80.1%

Length

2024-04-16T17:41:24.048614image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:41:24.241614image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
for 1160
 
6.0%
project 823
 
4.3%
encourage 458
 
2.4%
cross-functional 458
 
2.4%
collaboration 458
 
2.4%
conduct 455
 
2.4%
regular 455
 
2.4%
retrospectives 455
 
2.4%
learning 455
 
2.4%
creep 415
 
2.2%
Other values (36) 13595
70.9%

Most occurring characters

ValueCountFrequency (%)
e 18152
11.1%
15218
 
9.3%
t 13696
 
8.4%
r 12846
 
7.9%
n 12262
 
7.5%
o 11813
 
7.3%
a 10340
 
6.3%
i 8598
 
5.3%
s 8232
 
5.1%
c 8011
 
4.9%
Other values (23) 43699
26.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 162867
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 18152
11.1%
15218
 
9.3%
t 13696
 
8.4%
r 12846
 
7.9%
n 12262
 
7.5%
o 11813
 
7.3%
a 10340
 
6.3%
i 8598
 
5.3%
s 8232
 
5.1%
c 8011
 
4.9%
Other values (23) 43699
26.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 162867
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 18152
11.1%
15218
 
9.3%
t 13696
 
8.4%
r 12846
 
7.9%
n 12262
 
7.5%
o 11813
 
7.3%
a 10340
 
6.3%
i 8598
 
5.3%
s 8232
 
5.1%
c 8011
 
4.9%
Other values (23) 43699
26.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 162867
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 18152
11.1%
15218
 
9.3%
t 13696
 
8.4%
r 12846
 
7.9%
n 12262
 
7.5%
o 11813
 
7.3%
a 10340
 
6.3%
i 8598
 
5.3%
s 8232
 
5.1%
c 8011
 
4.9%
Other values (23) 43699
26.8%
Distinct105
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.0 KiB
Minimum2022-04-10 00:00:00
Maximum2024-04-07 00:00:00
2024-04-16T17:41:24.510631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:24.703615image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct105
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.0 KiB
Minimum2022-04-16 00:00:00
Maximum2024-04-13 00:00:00
2024-04-16T17:41:24.899631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:25.099616image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

USERID
Real number (ℝ)

Distinct1008
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1033.5941
Minimum1
Maximum1999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.0 KiB
2024-04-16T17:41:25.306631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile105
Q1526
median1043
Q31562
95-th percentile1903
Maximum1999
Range1998
Interquartile range (IQR)1036

Descriptive statistics

Standard deviation582.44069
Coefficient of variation (CV)0.56351008
Kurtosis-1.2176576
Mean1033.5941
Median Absolute Deviation (MAD)517
Skewness-0.078145494
Sum20625370
Variance339237.16
MonotonicityNot monotonic
2024-04-16T17:41:25.516615image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1337 209
 
1.0%
937 182
 
0.9%
1216 176
 
0.9%
1563 156
 
0.8%
711 140
 
0.7%
1307 135
 
0.7%
1890 132
 
0.7%
477 128
 
0.6%
206 120
 
0.6%
1772 120
 
0.6%
Other values (998) 18457
92.5%
ValueCountFrequency (%)
1 1
 
< 0.1%
3 4
 
< 0.1%
4 3
 
< 0.1%
9 18
0.1%
10 24
0.1%
11 1
 
< 0.1%
13 2
 
< 0.1%
14 20
0.1%
18 28
0.1%
19 12
0.1%
ValueCountFrequency (%)
1999 6
 
< 0.1%
1997 40
0.2%
1994 20
 
0.1%
1989 6
 
< 0.1%
1986 6
 
< 0.1%
1985 2
 
< 0.1%
1984 1
 
< 0.1%
1983 9
 
< 0.1%
1979 49
0.2%
1977 91
0.5%

PROJECTTYPE
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.0 KiB
FullStack
6725 
Data Engineering
6615 
Data Science & Analytics
6615 

Length

Max length24
Median length16
Mean length16.292909
Min length9

Characters and Unicode

Total characters325125
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowData Engineering
2nd rowData Science & Analytics
3rd rowData Science & Analytics
4th rowData Engineering
5th rowData Engineering

Common Values

ValueCountFrequency (%)
FullStack 6725
33.7%
Data Engineering 6615
33.1%
Data Science & Analytics 6615
33.1%

Length

2024-04-16T17:41:25.721632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:41:25.875633image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
data 13230
28.5%
fullstack 6725
14.5%
engineering 6615
14.3%
science 6615
14.3%
6615
14.3%
analytics 6615
14.3%

Most occurring characters

ValueCountFrequency (%)
a 39800
12.2%
n 33075
10.2%
t 26570
 
8.2%
c 26570
 
8.2%
26460
 
8.1%
i 26460
 
8.1%
e 26460
 
8.1%
l 20065
 
6.2%
S 13340
 
4.1%
D 13230
 
4.1%
Other values (10) 73095
22.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 325125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 39800
12.2%
n 33075
10.2%
t 26570
 
8.2%
c 26570
 
8.2%
26460
 
8.1%
i 26460
 
8.1%
e 26460
 
8.1%
l 20065
 
6.2%
S 13340
 
4.1%
D 13230
 
4.1%
Other values (10) 73095
22.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 325125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 39800
12.2%
n 33075
10.2%
t 26570
 
8.2%
c 26570
 
8.2%
26460
 
8.1%
i 26460
 
8.1%
e 26460
 
8.1%
l 20065
 
6.2%
S 13340
 
4.1%
D 13230
 
4.1%
Other values (10) 73095
22.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 325125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 39800
12.2%
n 33075
10.2%
t 26570
 
8.2%
c 26570
 
8.2%
26460
 
8.1%
i 26460
 
8.1%
e 26460
 
8.1%
l 20065
 
6.2%
S 13340
 
4.1%
D 13230
 
4.1%
Other values (10) 73095
22.5%

TASKSELECTED
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.0 KiB
['Deployment']
2366 
['Documentation']
2361 
['Frontend']
2252 
['Ingestion&Transformation']
2222 
['Integration&Optimization']
2214 
Other values (4)
8540 

Length

Max length38
Median length26
Mean length20.787472
Min length11

Characters and Unicode

Total characters414814
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row['Prediction,Clustering&Optimization']
2nd row['Backend']
3rd row['Backend']
4th row['Deployment']
5th row['Deployment']

Common Values

ValueCountFrequency (%)
['Deployment'] 2366
11.9%
['Documentation'] 2361
11.8%
['Frontend'] 2252
11.3%
['Ingestion&Transformation'] 2222
11.1%
['Integration&Optimization'] 2214
11.1%
['Prediction,Clustering&Optimization'] 2166
10.9%
['Backend'] 2161
10.8%
['QA&Testing'] 2108
10.6%
['Modeling&Visualization'] 2105
10.5%

Length

2024-04-16T17:41:26.036631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:41:26.215617image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
deployment 2366
11.9%
documentation 2361
11.8%
frontend 2252
11.3%
ingestion&transformation 2222
11.1%
integration&optimization 2214
11.1%
prediction,clustering&optimization 2166
10.9%
backend 2161
10.8%
qa&testing 2108
10.6%
modeling&visualization 2105
10.5%

Most occurring characters

ValueCountFrequency (%)
n 42099
 
10.1%
' 39910
 
9.6%
i 39185
 
9.4%
t 35517
 
8.6%
o 28976
 
7.0%
e 24487
 
5.9%
[ 19955
 
4.8%
] 19955
 
4.8%
a 19770
 
4.8%
r 13242
 
3.2%
Other values (26) 131718
31.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 414814
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 42099
 
10.1%
' 39910
 
9.6%
i 39185
 
9.4%
t 35517
 
8.6%
o 28976
 
7.0%
e 24487
 
5.9%
[ 19955
 
4.8%
] 19955
 
4.8%
a 19770
 
4.8%
r 13242
 
3.2%
Other values (26) 131718
31.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 414814
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 42099
 
10.1%
' 39910
 
9.6%
i 39185
 
9.4%
t 35517
 
8.6%
o 28976
 
7.0%
e 24487
 
5.9%
[ 19955
 
4.8%
] 19955
 
4.8%
a 19770
 
4.8%
r 13242
 
3.2%
Other values (26) 131718
31.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 414814
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 42099
 
10.1%
' 39910
 
9.6%
i 39185
 
9.4%
t 35517
 
8.6%
o 28976
 
7.0%
e 24487
 
5.9%
[ 19955
 
4.8%
] 19955
 
4.8%
a 19770
 
4.8%
r 13242
 
3.2%
Other values (26) 131718
31.8%

MON
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5291907
Minimum4
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.0 KiB
2024-04-16T17:41:26.428616image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q17
median9
Q310
95-th percentile12
Maximum13
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9636333
Coefficient of variation (CV)0.23022505
Kurtosis-0.48994677
Mean8.5291907
Median Absolute Deviation (MAD)1
Skewness0.0037424515
Sum170200
Variance3.8558558
MonotonicityNot monotonic
2024-04-16T17:41:26.568632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
9 3808
19.1%
8 3465
17.4%
10 3137
15.7%
7 3107
15.6%
11 1934
9.7%
6 1930
9.7%
12 951
 
4.8%
5 947
 
4.7%
13 376
 
1.9%
4 300
 
1.5%
ValueCountFrequency (%)
4 300
 
1.5%
5 947
 
4.7%
6 1930
9.7%
7 3107
15.6%
8 3465
17.4%
9 3808
19.1%
10 3137
15.7%
11 1934
9.7%
12 951
 
4.8%
13 376
 
1.9%
ValueCountFrequency (%)
13 376
 
1.9%
12 951
 
4.8%
11 1934
9.7%
10 3137
15.7%
9 3808
19.1%
8 3465
17.4%
7 3107
15.6%
6 1930
9.7%
5 947
 
4.7%
4 300
 
1.5%

TUE
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4640942
Minimum4
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.0 KiB
2024-04-16T17:41:26.707631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q17
median8
Q310
95-th percentile12
Maximum13
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.002542
Coefficient of variation (CV)0.2365926
Kurtosis-0.54571535
Mean8.4640942
Median Absolute Deviation (MAD)1
Skewness0.01302688
Sum168901
Variance4.0101746
MonotonicityNot monotonic
2024-04-16T17:41:26.847631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
9 3733
18.7%
8 3525
17.7%
7 3117
15.6%
10 2750
13.8%
6 2029
10.2%
11 2000
10.0%
12 1070
 
5.4%
5 1039
 
5.2%
4 380
 
1.9%
13 312
 
1.6%
ValueCountFrequency (%)
4 380
 
1.9%
5 1039
 
5.2%
6 2029
10.2%
7 3117
15.6%
8 3525
17.7%
9 3733
18.7%
10 2750
13.8%
11 2000
10.0%
12 1070
 
5.4%
13 312
 
1.6%
ValueCountFrequency (%)
13 312
 
1.6%
12 1070
 
5.4%
11 2000
10.0%
10 2750
13.8%
9 3733
18.7%
8 3525
17.7%
7 3117
15.6%
6 2029
10.2%
5 1039
 
5.2%
4 380
 
1.9%

WED
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5077424
Minimum4
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.0 KiB
2024-04-16T17:41:26.985632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q17
median9
Q310
95-th percentile12
Maximum13
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9628651
Coefficient of variation (CV)0.23071515
Kurtosis-0.45756606
Mean8.5077424
Median Absolute Deviation (MAD)1
Skewness-0.035556491
Sum169772
Variance3.8528392
MonotonicityNot monotonic
2024-04-16T17:41:27.118615image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
9 3824
19.2%
8 3588
18.0%
7 3019
15.1%
10 3018
15.1%
11 2091
10.5%
6 1942
9.7%
5 880
 
4.4%
12 850
 
4.3%
4 397
 
2.0%
13 346
 
1.7%
ValueCountFrequency (%)
4 397
 
2.0%
5 880
 
4.4%
6 1942
9.7%
7 3019
15.1%
8 3588
18.0%
9 3824
19.2%
10 3018
15.1%
11 2091
10.5%
12 850
 
4.3%
13 346
 
1.7%
ValueCountFrequency (%)
13 346
 
1.7%
12 850
 
4.3%
11 2091
10.5%
10 3018
15.1%
9 3824
19.2%
8 3588
18.0%
7 3019
15.1%
6 1942
9.7%
5 880
 
4.4%
4 397
 
2.0%

THU
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4884991
Minimum4
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.0 KiB
2024-04-16T17:41:27.256631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q17
median8
Q310
95-th percentile12
Maximum13
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9713813
Coefficient of variation (CV)0.23224144
Kurtosis-0.49668073
Mean8.4884991
Median Absolute Deviation (MAD)1
Skewness-0.018570598
Sum169388
Variance3.8863441
MonotonicityNot monotonic
2024-04-16T17:41:27.396631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
8 3736
18.7%
9 3607
18.1%
7 3093
15.5%
10 2994
15.0%
11 2033
10.2%
6 1851
9.3%
5 1011
 
5.1%
12 956
 
4.8%
4 368
 
1.8%
13 306
 
1.5%
ValueCountFrequency (%)
4 368
 
1.8%
5 1011
 
5.1%
6 1851
9.3%
7 3093
15.5%
8 3736
18.7%
9 3607
18.1%
10 2994
15.0%
11 2033
10.2%
12 956
 
4.8%
13 306
 
1.5%
ValueCountFrequency (%)
13 306
 
1.5%
12 956
 
4.8%
11 2033
10.2%
10 2994
15.0%
9 3607
18.1%
8 3736
18.7%
7 3093
15.5%
6 1851
9.3%
5 1011
 
5.1%
4 368
 
1.8%

FRI
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5035831
Minimum4
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.0 KiB
2024-04-16T17:41:27.840616image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q17
median8
Q310
95-th percentile12
Maximum13
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.943995
Coefficient of variation (CV)0.22860893
Kurtosis-0.50170558
Mean8.5035831
Median Absolute Deviation (MAD)1
Skewness0.043425677
Sum169689
Variance3.7791167
MonotonicityNot monotonic
2024-04-16T17:41:27.980616image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
9 3776
18.9%
8 3760
18.8%
7 3132
15.7%
10 2867
14.4%
11 1986
10.0%
6 1857
9.3%
5 1069
 
5.4%
12 953
 
4.8%
13 338
 
1.7%
4 217
 
1.1%
ValueCountFrequency (%)
4 217
 
1.1%
5 1069
 
5.4%
6 1857
9.3%
7 3132
15.7%
8 3760
18.8%
9 3776
18.9%
10 2867
14.4%
11 1986
10.0%
12 953
 
4.8%
13 338
 
1.7%
ValueCountFrequency (%)
13 338
 
1.7%
12 953
 
4.8%
11 1986
10.0%
10 2867
14.4%
9 3776
18.9%
8 3760
18.8%
7 3132
15.7%
6 1857
9.3%
5 1069
 
5.4%
4 217
 
1.1%

SAT
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5203207
Minimum4
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.0 KiB
2024-04-16T17:41:28.122615image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q17
median9
Q310
95-th percentile12
Maximum13
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0116742
Coefficient of variation (CV)0.23610311
Kurtosis-0.54661016
Mean8.5203207
Median Absolute Deviation (MAD)1
Skewness-0.0019979281
Sum170023
Variance4.0468332
MonotonicityNot monotonic
2024-04-16T17:41:28.298615image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
8 3645
18.3%
9 3452
17.3%
10 3108
15.6%
7 2890
14.5%
6 2050
10.3%
11 2049
10.3%
5 1008
 
5.1%
12 997
 
5.0%
13 402
 
2.0%
4 354
 
1.8%
ValueCountFrequency (%)
4 354
 
1.8%
5 1008
 
5.1%
6 2050
10.3%
7 2890
14.5%
8 3645
18.3%
9 3452
17.3%
10 3108
15.6%
11 2049
10.3%
12 997
 
5.0%
13 402
 
2.0%
ValueCountFrequency (%)
13 402
 
2.0%
12 997
 
5.0%
11 2049
10.3%
10 3108
15.6%
9 3452
17.3%
8 3645
18.3%
7 2890
14.5%
6 2050
10.3%
5 1008
 
5.1%
4 354
 
1.8%

SUN
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.541769
Minimum4
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.0 KiB
2024-04-16T17:41:28.450615image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q17
median9
Q310
95-th percentile12
Maximum13
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9889181
Coefficient of variation (CV)0.23284616
Kurtosis-0.54761074
Mean8.541769
Median Absolute Deviation (MAD)1
Skewness-0.040936143
Sum170451
Variance3.9557951
MonotonicityNot monotonic
2024-04-16T17:41:28.601616image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
9 3616
18.1%
8 3470
17.4%
10 3175
15.9%
7 3038
15.2%
11 2064
10.3%
6 1865
9.3%
12 1058
 
5.3%
5 1012
 
5.1%
4 340
 
1.7%
13 317
 
1.6%
ValueCountFrequency (%)
4 340
 
1.7%
5 1012
 
5.1%
6 1865
9.3%
7 3038
15.2%
8 3470
17.4%
9 3616
18.1%
10 3175
15.9%
11 2064
10.3%
12 1058
 
5.3%
13 317
 
1.6%
ValueCountFrequency (%)
13 317
 
1.6%
12 1058
 
5.3%
11 2064
10.3%
10 3175
15.9%
9 3616
18.1%
8 3470
17.4%
7 3038
15.2%
6 1865
9.3%
5 1012
 
5.1%
4 340
 
1.7%

TOTAL
Real number (ℝ)

Distinct58
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.555199
Minimum30
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.0 KiB
2024-04-16T17:41:28.767631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile42
Q152
median59
Q367
95-th percentile77
Maximum89
Range59
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.378735
Coefficient of variation (CV)0.17427085
Kurtosis-0.55415823
Mean59.555199
Median Absolute Deviation (MAD)8
Skewness0.009292843
Sum1188424
Variance107.71815
MonotonicityNot monotonic
2024-04-16T17:41:28.960613image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57 784
 
3.9%
63 777
 
3.9%
61 758
 
3.8%
59 736
 
3.7%
58 719
 
3.6%
67 702
 
3.5%
56 674
 
3.4%
55 666
 
3.3%
52 654
 
3.3%
65 633
 
3.2%
Other values (48) 12852
64.4%
ValueCountFrequency (%)
30 3
 
< 0.1%
31 4
 
< 0.1%
33 6
 
< 0.1%
34 18
 
0.1%
35 29
 
0.1%
36 40
 
0.2%
37 75
0.4%
38 143
0.7%
39 149
0.7%
40 142
0.7%
ValueCountFrequency (%)
89 7
 
< 0.1%
87 9
 
< 0.1%
86 14
 
0.1%
85 5
 
< 0.1%
84 26
 
0.1%
83 58
 
0.3%
82 76
 
0.4%
81 106
0.5%
80 203
1.0%
79 170
0.9%

PROJECTNAME
Categorical

Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.0 KiB
Cox-Hays Solution
 
1113
White LLC Initiative
 
1109
Miller LLC Initiative
 
1091
Robinson and Sons Project
 
1072
Hicks and Sons Initiative
 
1063
Other values (15)
14507 

Length

Max length38
Median length32
Mean length24.661188
Min length17

Characters and Unicode

Total characters492114
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWashington-Little Initiative
2nd rowNeal PLC Initiative
3rd rowNeal PLC Initiative
4th rowWashington-Little Initiative
5th rowWashington-Little Initiative

Common Values

ValueCountFrequency (%)
Cox-Hays Solution 1113
 
5.6%
White LLC Initiative 1109
 
5.6%
Miller LLC Initiative 1091
 
5.5%
Robinson and Sons Project 1072
 
5.4%
Hicks and Sons Initiative 1063
 
5.3%
Neal PLC Initiative 1045
 
5.2%
Willis Ltd Solution 1040
 
5.2%
Jenkins-Johnson Initiative 1016
 
5.1%
Villarreal-Flynn Project 1008
 
5.1%
Johnson, Roth and Ray Initiative 1007
 
5.0%
Other values (10) 9391
47.1%

Length

2024-04-16T17:41:29.143631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
initiative 9991
 
16.1%
and 5994
 
9.7%
project 5923
 
9.6%
solution 4041
 
6.5%
llc 2200
 
3.5%
sons 2135
 
3.4%
miller 2091
 
3.4%
cox-hays 1113
 
1.8%
white 1109
 
1.8%
robinson 1072
 
1.7%
Other values (27) 26330
42.5%

Most occurring characters

ValueCountFrequency (%)
i 52020
 
10.6%
n 46282
 
9.4%
42044
 
8.5%
t 38704
 
7.9%
o 33690
 
6.8%
a 33319
 
6.8%
e 30585
 
6.2%
l 21589
 
4.4%
r 20471
 
4.2%
s 16905
 
3.4%
Other values (35) 156505
31.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 492114
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 52020
 
10.6%
n 46282
 
9.4%
42044
 
8.5%
t 38704
 
7.9%
o 33690
 
6.8%
a 33319
 
6.8%
e 30585
 
6.2%
l 21589
 
4.4%
r 20471
 
4.2%
s 16905
 
3.4%
Other values (35) 156505
31.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 492114
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 52020
 
10.6%
n 46282
 
9.4%
42044
 
8.5%
t 38704
 
7.9%
o 33690
 
6.8%
a 33319
 
6.8%
e 30585
 
6.2%
l 21589
 
4.4%
r 20471
 
4.2%
s 16905
 
3.4%
Other values (35) 156505
31.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 492114
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 52020
 
10.6%
n 46282
 
9.4%
42044
 
8.5%
t 38704
 
7.9%
o 33690
 
6.8%
a 33319
 
6.8%
e 30585
 
6.2%
l 21589
 
4.4%
r 20471
 
4.2%
s 16905
 
3.4%
Other values (35) 156505
31.8%

DOMAIN
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.0 KiB
Cybersecurity
4909 
Media and Entertainment
4192 
Financial Services
2957 
Healthcare
2080 
Telecommunications
2003 
Other values (4)
3814 

Length

Max length44
Median length23
Mean length18.613731
Min length10

Characters and Unicode

Total characters371437
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedia and Entertainment
2nd rowTelecommunications
3rd rowTelecommunications
4th rowMedia and Entertainment
5th rowMedia and Entertainment

Common Values

ValueCountFrequency (%)
Cybersecurity 4909
24.6%
Media and Entertainment 4192
21.0%
Financial Services 2957
14.8%
Healthcare 2080
10.4%
Telecommunications 2003
10.0%
Education Technology 1040
 
5.2%
Renewable Energy 1007
 
5.0%
Artificial Intelligence and Machine Learning 921
 
4.6%
Transportation and Logistics 846
 
4.2%

Length

2024-04-16T17:41:29.315616image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:41:29.502632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
and 5959
15.4%
cybersecurity 4909
12.7%
media 4192
10.8%
entertainment 4192
10.8%
financial 2957
7.6%
services 2957
7.6%
healthcare 2080
 
5.4%
telecommunications 2003
 
5.2%
technology 1040
 
2.7%
education 1040
 
2.7%
Other values (8) 7390
19.1%

Most occurring characters

ValueCountFrequency (%)
e 46147
12.4%
n 38846
 
10.5%
i 35274
 
9.5%
a 32922
 
8.9%
t 26988
 
7.3%
r 23588
 
6.4%
c 22598
 
6.1%
18764
 
5.1%
s 12407
 
3.3%
y 11865
 
3.2%
Other values (23) 102038
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 371437
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 46147
12.4%
n 38846
 
10.5%
i 35274
 
9.5%
a 32922
 
8.9%
t 26988
 
7.3%
r 23588
 
6.4%
c 22598
 
6.1%
18764
 
5.1%
s 12407
 
3.3%
y 11865
 
3.2%
Other values (23) 102038
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 371437
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 46147
12.4%
n 38846
 
10.5%
i 35274
 
9.5%
a 32922
 
8.9%
t 26988
 
7.3%
r 23588
 
6.4%
c 22598
 
6.1%
18764
 
5.1%
s 12407
 
3.3%
y 11865
 
3.2%
Other values (23) 102038
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 371437
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 46147
12.4%
n 38846
 
10.5%
i 35274
 
9.5%
a 32922
 
8.9%
t 26988
 
7.3%
r 23588
 
6.4%
c 22598
 
6.1%
18764
 
5.1%
s 12407
 
3.3%
y 11865
 
3.2%
Other values (23) 102038
27.5%

COMPLEXITY
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.0 KiB
High
14139 
Medium
5816 

Length

Max length6
Median length4
Mean length4.5829116
Min length4

Characters and Unicode

Total characters91452
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh
2nd rowHigh
3rd rowHigh
4th rowHigh
5th rowHigh

Common Values

ValueCountFrequency (%)
High 14139
70.9%
Medium 5816
29.1%

Length

2024-04-16T17:41:29.735631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:41:29.890632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
high 14139
70.9%
medium 5816
29.1%

Most occurring characters

ValueCountFrequency (%)
i 19955
21.8%
H 14139
15.5%
g 14139
15.5%
h 14139
15.5%
M 5816
 
6.4%
e 5816
 
6.4%
d 5816
 
6.4%
u 5816
 
6.4%
m 5816
 
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 91452
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 19955
21.8%
H 14139
15.5%
g 14139
15.5%
h 14139
15.5%
M 5816
 
6.4%
e 5816
 
6.4%
d 5816
 
6.4%
u 5816
 
6.4%
m 5816
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 91452
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 19955
21.8%
H 14139
15.5%
g 14139
15.5%
h 14139
15.5%
M 5816
 
6.4%
e 5816
 
6.4%
d 5816
 
6.4%
u 5816
 
6.4%
m 5816
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 91452
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 19955
21.8%
H 14139
15.5%
g 14139
15.5%
h 14139
15.5%
M 5816
 
6.4%
e 5816
 
6.4%
d 5816
 
6.4%
u 5816
 
6.4%
m 5816
 
6.4%

USER_ID
Real number (ℝ)

Distinct1008
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1033.5941
Minimum1
Maximum1999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.0 KiB
2024-04-16T17:41:30.053631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile105
Q1526
median1043
Q31562
95-th percentile1903
Maximum1999
Range1998
Interquartile range (IQR)1036

Descriptive statistics

Standard deviation582.44069
Coefficient of variation (CV)0.56351008
Kurtosis-1.2176576
Mean1033.5941
Median Absolute Deviation (MAD)517
Skewness-0.078145494
Sum20625370
Variance339237.16
MonotonicityNot monotonic
2024-04-16T17:41:30.277616image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1337 209
 
1.0%
937 182
 
0.9%
1216 176
 
0.9%
1563 156
 
0.8%
711 140
 
0.7%
1307 135
 
0.7%
1890 132
 
0.7%
477 128
 
0.6%
206 120
 
0.6%
1772 120
 
0.6%
Other values (998) 18457
92.5%
ValueCountFrequency (%)
1 1
 
< 0.1%
3 4
 
< 0.1%
4 3
 
< 0.1%
9 18
0.1%
10 24
0.1%
11 1
 
< 0.1%
13 2
 
< 0.1%
14 20
0.1%
18 28
0.1%
19 12
0.1%
ValueCountFrequency (%)
1999 6
 
< 0.1%
1997 40
0.2%
1994 20
 
0.1%
1989 6
 
< 0.1%
1986 6
 
< 0.1%
1985 2
 
< 0.1%
1984 1
 
< 0.1%
1983 9
 
< 0.1%
1979 49
0.2%
1977 91
0.5%

NAME
Text

Distinct1002
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size156.0 KiB
2024-04-16T17:41:30.812433image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length22
Median length19
Mean length12.989927
Min length7

Characters and Unicode

Total characters259214
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique88 ?
Unique (%)0.4%

Sample

1st rowAaron Waller
2nd rowJennifer Cohen
3rd rowJennifer Cohen
4th rowTodd Swanson
5th rowTodd Swanson
ValueCountFrequency (%)
davis 648
 
1.6%
smith 641
 
1.6%
johnson 596
 
1.5%
michael 555
 
1.4%
matthew 424
 
1.1%
david 389
 
1.0%
jessica 387
 
1.0%
thomas 358
 
0.9%
john 357
 
0.9%
edwards 354
 
0.9%
Other values (794) 35201
88.2%
2024-04-16T17:41:31.574832image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 24836
 
9.6%
e 24438
 
9.4%
19955
 
7.7%
n 19023
 
7.3%
r 17813
 
6.9%
i 16049
 
6.2%
o 13286
 
5.1%
s 12725
 
4.9%
l 11312
 
4.4%
h 9561
 
3.7%
Other values (40) 90216
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 259214
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 24836
 
9.6%
e 24438
 
9.4%
19955
 
7.7%
n 19023
 
7.3%
r 17813
 
6.9%
i 16049
 
6.2%
o 13286
 
5.1%
s 12725
 
4.9%
l 11312
 
4.4%
h 9561
 
3.7%
Other values (40) 90216
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 259214
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 24836
 
9.6%
e 24438
 
9.4%
19955
 
7.7%
n 19023
 
7.3%
r 17813
 
6.9%
i 16049
 
6.2%
o 13286
 
5.1%
s 12725
 
4.9%
l 11312
 
4.4%
h 9561
 
3.7%
Other values (40) 90216
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 259214
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 24836
 
9.6%
e 24438
 
9.4%
19955
 
7.7%
n 19023
 
7.3%
r 17813
 
6.9%
i 16049
 
6.2%
o 13286
 
5.1%
s 12725
 
4.9%
l 11312
 
4.4%
h 9561
 
3.7%
Other values (40) 90216
34.8%

ROLE
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.0 KiB
Intern
10884 
Jr.Developer
3629 
Sr.Developer
3618 
Admin
1297 
Solutions Consultant
 
527

Length

Max length20
Median length6
Mean length8.4837384
Min length5

Characters and Unicode

Total characters169293
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJr.Developer
2nd rowIntern
3rd rowIntern
4th rowIntern
5th rowIntern

Common Values

ValueCountFrequency (%)
Intern 10884
54.5%
Jr.Developer 3629
 
18.2%
Sr.Developer 3618
 
18.1%
Admin 1297
 
6.5%
Solutions Consultant 527
 
2.6%

Length

2024-04-16T17:41:31.804821image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T17:41:31.973844image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
intern 10884
53.1%
jr.developer 3629
 
17.7%
sr.developer 3618
 
17.7%
admin 1297
 
6.3%
solutions 527
 
2.6%
consultant 527
 
2.6%

Most occurring characters

ValueCountFrequency (%)
e 32625
19.3%
r 25378
15.0%
n 24646
14.6%
t 12465
 
7.4%
I 10884
 
6.4%
o 8828
 
5.2%
l 8301
 
4.9%
v 7247
 
4.3%
p 7247
 
4.3%
D 7247
 
4.3%
Other values (12) 24425
14.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 169293
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 32625
19.3%
r 25378
15.0%
n 24646
14.6%
t 12465
 
7.4%
I 10884
 
6.4%
o 8828
 
5.2%
l 8301
 
4.9%
v 7247
 
4.3%
p 7247
 
4.3%
D 7247
 
4.3%
Other values (12) 24425
14.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 169293
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 32625
19.3%
r 25378
15.0%
n 24646
14.6%
t 12465
 
7.4%
I 10884
 
6.4%
o 8828
 
5.2%
l 8301
 
4.9%
v 7247
 
4.3%
p 7247
 
4.3%
D 7247
 
4.3%
Other values (12) 24425
14.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 169293
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 32625
19.3%
r 25378
15.0%
n 24646
14.6%
t 12465
 
7.4%
I 10884
 
6.4%
o 8828
 
5.2%
l 8301
 
4.9%
v 7247
 
4.3%
p 7247
 
4.3%
D 7247
 
4.3%
Other values (12) 24425
14.4%

Interactions

2024-04-16T17:41:18.035614image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:06.178042image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:07.762058image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:08.975730image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:10.280732image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:11.582731image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:13.012633image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:14.217617image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:15.563632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:16.784632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:18.179631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:06.539058image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:07.902731image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:09.123729image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:10.462731image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:11.940632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:13.151631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:14.354617image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:15.702616image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:16.922632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:18.542616image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:06.670058image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:08.024747image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:09.246746image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:10.597731image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:12.062631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:13.262616image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:14.479617image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:15.819627image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:17.046632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:18.667616image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:06.795058image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:08.136747image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:09.364746image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:10.714731image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:12.179631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:13.369617image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:14.602632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:15.936616image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:17.163632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:18.795631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:06.926058image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:08.245732image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:09.482749image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:10.836746image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:12.291632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:13.479616image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:14.726617image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:16.055632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:17.281631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:18.920616image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:07.062052image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:08.368731image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:09.593730image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:10.957746image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:12.407615image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:13.591632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:14.927617image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:16.178614image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:17.397633image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:19.054615image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:07.192060image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:08.479731image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:09.713741image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:11.074747image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:12.525632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:13.701632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:15.053634image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:16.295615image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:17.515632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:19.181632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:07.317041image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:08.588743image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:09.835729image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:11.186747image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:12.638632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:13.820614image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:15.177641image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:16.405617image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:17.638636image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:19.308632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:07.444060image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:08.702746image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:09.961730image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:11.307728image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:12.749633image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:13.935616image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:15.302645image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:16.521632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:17.755633image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:19.457620image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:07.598042image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:08.836744image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:10.134729image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:11.433747image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:12.871633image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:14.074633image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:15.428617image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:16.650614image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-16T17:41:17.886631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Missing values

2024-04-16T17:41:19.720615image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-16T17:41:20.436615image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PROJECTASSIGNEDSATISFACTIONCOMMUNICATIONGOALSDELIVERABLESTIMELINESSCHALLENGESPROJECTMANAGEMENTSUPPORTIMPROVEMENTDATESTARTDATEENDUSERIDPROJECTTYPETASKSELECTEDMONTUEWEDTHUFRISATSUNTOTALPROJECTNAMEDOMAINCOMPLEXITYUSER_IDNAMEROLE
0Washington-Little Initiative1.01.04.04.01.02.04.04.0NaN2023-10-222023-10-28283Data Engineering['Prediction,Clustering&Optimization']910891210967Washington-Little InitiativeMedia and EntertainmentHigh283Aaron WallerJr.Developer
1Neal PLC Initiative2.03.02.05.03.03.05.05.0NaN2022-07-242022-07-30430Data Science & Analytics['Backend']9998891062Neal PLC InitiativeTelecommunicationsHigh430Jennifer CohenIntern
2Neal PLC Initiative1.04.0NaN3.03.01.03.02.0NaN2024-01-142024-01-20430Data Science & Analytics['Backend']9998891062Neal PLC InitiativeTelecommunicationsHigh430Jennifer CohenIntern
3Washington-Little Initiative4.05.02.01.04.04.04.05.0NaN2023-02-122023-02-18309Data Engineering['Deployment']766966949Washington-Little InitiativeMedia and EntertainmentHigh309Todd SwansonIntern
4Washington-Little InitiativeNaN3.03.04.04.02.03.01.0NaN2023-02-122023-02-18309Data Engineering['Deployment']766966949Washington-Little InitiativeMedia and EntertainmentHigh309Todd SwansonIntern
5Washington-Little InitiativeNaN4.0NaN5.05.04.01.03.0NaN2023-09-242023-09-30309Data Engineering['Deployment']766966949Washington-Little InitiativeMedia and EntertainmentHigh309Todd SwansonIntern
6Washington-Little InitiativeNaN1.04.0NaN3.02.0NaN5.0NaN2023-02-122023-02-18309Data Engineering['Deployment']766966949Washington-Little InitiativeMedia and EntertainmentHigh309Todd SwansonIntern
7Medina, Miller and Craig Initiative3.03.02.02.0NaN4.05.05.0NaN2022-08-142022-08-20309Data Engineering['Deployment']766966949Medina, Miller and Craig InitiativeFinancial ServicesHigh309Todd SwansonIntern
8Washington-Little Initiative2.03.0NaN5.02.0NaN2.02.0NaN2023-09-242023-09-30309Data Engineering['Deployment']766966949Washington-Little InitiativeMedia and EntertainmentHigh309Todd SwansonIntern
9Flowers-Anderson Project1.0NaN3.0NaN4.03.0NaN5.0NaN2023-12-312024-01-06600Data Science & Analytics['Backend']8679861054Flowers-Anderson ProjectCybersecurityMedium600Bruce DuarteSr.Developer
PROJECTASSIGNEDSATISFACTIONCOMMUNICATIONGOALSDELIVERABLESTIMELINESSCHALLENGESPROJECTMANAGEMENTSUPPORTIMPROVEMENTDATESTARTDATEENDUSERIDPROJECTTYPETASKSELECTEDMONTUEWEDTHUFRISATSUNTOTALPROJECTNAMEDOMAINCOMPLEXITYUSER_IDNAMEROLE
19945Smith, Francis and Nelson ProjectNaN5.03.0NaN3.05.02.01.0NaN2024-02-112024-02-171890FullStack['Backend']777957951Smith, Francis and Nelson ProjectTelecommunicationsMedium1890John EdwardsIntern
19946Smith, Francis and Nelson Project3.02.03.01.03.02.04.01.0NaN2022-07-312022-08-061890FullStack['Backend']777957951Smith, Francis and Nelson ProjectTelecommunicationsMedium1890John EdwardsIntern
19947Perkins-Crawford Project1.0NaNNaN2.05.03.01.04.0NaN2022-04-102022-04-161890FullStack['Backend']777957951Perkins-Crawford ProjectCybersecurityHigh1890John EdwardsIntern
19948Smith, Francis and Nelson Project3.04.04.0NaN5.0NaN3.04.0NaN2022-05-222022-05-281890FullStack['Backend']777957951Smith, Francis and Nelson ProjectTelecommunicationsMedium1890John EdwardsIntern
19949Smith, Francis and Nelson Project5.05.03.01.02.0NaNNaN4.0NaN2024-02-112024-02-171890FullStack['Backend']777957951Smith, Francis and Nelson ProjectTelecommunicationsMedium1890John EdwardsIntern
19950Smith, Francis and Nelson Project5.0NaN3.05.04.0NaNNaN1.0NaN2022-07-032022-07-091890FullStack['Backend']777957951Smith, Francis and Nelson ProjectTelecommunicationsMedium1890John EdwardsIntern
19951Smith, Francis and Nelson Project5.02.03.03.04.0NaN4.01.0NaN2022-05-222022-05-281890FullStack['Backend']777957951Smith, Francis and Nelson ProjectTelecommunicationsMedium1890John EdwardsIntern
19952Smith, Francis and Nelson ProjectNaN4.03.04.04.0NaNNaN2.0NaN2024-02-112024-02-171890FullStack['Backend']777957951Smith, Francis and Nelson ProjectTelecommunicationsMedium1890John EdwardsIntern
19953Smith, Francis and Nelson Project4.04.02.0NaNNaN3.05.0NaNNaN2024-02-112024-02-171890FullStack['Backend']777957951Smith, Francis and Nelson ProjectTelecommunicationsMedium1890John EdwardsIntern
19954Smith, Francis and Nelson Project2.03.0NaN4.0NaN2.03.05.0NaN2022-07-032022-07-091890FullStack['Backend']777957951Smith, Francis and Nelson ProjectTelecommunicationsMedium1890John EdwardsIntern